Project Summary Identifying disease causing genetic variants is a complex process, often involving the analysis of the genomes of affected patients and their immediate families. When employing these state of the art techniques, however, the number of patients receiving a successful diagnosis is still only at approximately 30%. One reason for this low diagnosis rate is the presence of localised data quality problems, which are often missed in otherwise high quality data; for example, a single exon in a gene skipped in a sequencing experiment. This proposal aims to develop an easy-to-use, web-based application to assess the quality of sequencing data in localized regions, e.g. human genes, built on the IOBIO platform. An prototype algorithm has already been developed to generate the quality information, and has already identified a number of problems in high-quality sequencing data; problems that have led to institutional bioinformatic pipelines being amended, and sequencing providers to resequence samples. The proposed application will be built on top of this algorithm and will be complementary to other IOBIO quality control apps that assess large-scale data quality metrics, and together, provide a comprehensive assessment of the data across multiple scales. The app will be designed to allow rapid and intuitive assessment of multiple samples simultaneously, since common analysis projects involve multiple related individuals, all of whom must have high-quality sequencing data available. Core IOBIO functionality to integrate IOBIO apps together will be developed, so users can easily jump between the IOBIO apps most relevant to the problem at hand. As users move between different apps, necessary information will be shared between them to provide a seamless experience for the user. The objective of this proposal is to develop a commercially viable product to significantly improve quality control of sequencing data, based on proven quality control metrics. Ultimately, this will improve the rate of diagnosing patients, and improve the cost and time efficiency of sequencing analysis projects.